from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-05 14:06:39.349527
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Sat, 05, Dec, 2020
Time: 14:06:43
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -43.2908
Nobs: 131.000 HQIC: -44.4635
Log likelihood: 1382.01 FPE: 2.19947e-20
AIC: -45.2662 Det(Omega_mle): 1.13446e-20
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.555933 0.181652 3.060 0.002
L1.Burgenland 0.131388 0.086292 1.523 0.128
L1.Kärnten -0.309072 0.073074 -4.230 0.000
L1.Niederösterreich 0.077125 0.206665 0.373 0.709
L1.Oberösterreich 0.290476 0.172070 1.688 0.091
L1.Salzburg 0.150904 0.087285 1.729 0.084
L1.Steiermark 0.076630 0.124059 0.618 0.537
L1.Tirol 0.171900 0.082354 2.087 0.037
L1.Vorarlberg 0.020269 0.079552 0.255 0.799
L1.Wien -0.138973 0.163986 -0.847 0.397
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.566640 0.233407 2.428 0.015
L1.Burgenland -0.001195 0.110878 -0.011 0.991
L1.Kärnten 0.337874 0.093893 3.598 0.000
L1.Niederösterreich 0.109835 0.265545 0.414 0.679
L1.Oberösterreich -0.211528 0.221094 -0.957 0.339
L1.Salzburg 0.191130 0.112153 1.704 0.088
L1.Steiermark 0.234814 0.159404 1.473 0.141
L1.Tirol 0.140102 0.105818 1.324 0.186
L1.Vorarlberg 0.214225 0.102217 2.096 0.036
L1.Wien -0.560541 0.210708 -2.660 0.008
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.332507 0.079016 4.208 0.000
L1.Burgenland 0.101485 0.037536 2.704 0.007
L1.Kärnten -0.025452 0.031786 -0.801 0.423
L1.Niederösterreich 0.107965 0.089896 1.201 0.230
L1.Oberösterreich 0.281377 0.074848 3.759 0.000
L1.Salzburg -0.013910 0.037968 -0.366 0.714
L1.Steiermark -0.052589 0.053964 -0.975 0.330
L1.Tirol 0.098886 0.035823 2.760 0.006
L1.Vorarlberg 0.141976 0.034604 4.103 0.000
L1.Wien 0.036970 0.071332 0.518 0.604
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.186061 0.093101 1.998 0.046
L1.Burgenland 0.000778 0.044227 0.018 0.986
L1.Kärnten 0.031471 0.037452 0.840 0.401
L1.Niederösterreich 0.048620 0.105921 0.459 0.646
L1.Oberösterreich 0.374052 0.088190 4.241 0.000
L1.Salzburg 0.087857 0.044736 1.964 0.050
L1.Steiermark 0.202629 0.063583 3.187 0.001
L1.Tirol 0.036267 0.042209 0.859 0.390
L1.Vorarlberg 0.114040 0.040772 2.797 0.005
L1.Wien -0.083816 0.084047 -0.997 0.319
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.694621 0.198517 3.499 0.000
L1.Burgenland 0.063775 0.094303 0.676 0.499
L1.Kärnten -0.013055 0.079858 -0.163 0.870
L1.Niederösterreich -0.103302 0.225851 -0.457 0.647
L1.Oberösterreich 0.095398 0.188045 0.507 0.612
L1.Salzburg 0.036306 0.095388 0.381 0.703
L1.Steiermark 0.124446 0.135577 0.918 0.359
L1.Tirol 0.230706 0.090000 2.563 0.010
L1.Vorarlberg 0.042859 0.086937 0.493 0.622
L1.Wien -0.148852 0.179211 -0.831 0.406
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.247042 0.136565 1.809 0.070
L1.Burgenland -0.053648 0.064874 -0.827 0.408
L1.Kärnten -0.020767 0.054936 -0.378 0.705
L1.Niederösterreich 0.161132 0.155369 1.037 0.300
L1.Oberösterreich 0.396821 0.129360 3.068 0.002
L1.Salzburg -0.041658 0.065620 -0.635 0.526
L1.Steiermark -0.055598 0.093266 -0.596 0.551
L1.Tirol 0.202643 0.061913 3.273 0.001
L1.Vorarlberg 0.045579 0.059806 0.762 0.446
L1.Wien 0.132533 0.123283 1.075 0.282
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.242956 0.173557 1.400 0.162
L1.Burgenland 0.059848 0.082447 0.726 0.468
L1.Kärnten -0.080111 0.069818 -1.147 0.251
L1.Niederösterreich -0.101597 0.197455 -0.515 0.607
L1.Oberösterreich -0.080693 0.164401 -0.491 0.624
L1.Salzburg 0.011970 0.083395 0.144 0.886
L1.Steiermark 0.367110 0.118530 3.097 0.002
L1.Tirol 0.541922 0.078684 6.887 0.000
L1.Vorarlberg 0.235124 0.076007 3.093 0.002
L1.Wien -0.190783 0.156678 -1.218 0.223
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.054042 0.200224 0.270 0.787
L1.Burgenland 0.033169 0.095114 0.349 0.727
L1.Kärnten -0.068736 0.080545 -0.853 0.393
L1.Niederösterreich 0.195805 0.227793 0.860 0.390
L1.Oberösterreich 0.047704 0.189661 0.252 0.801
L1.Salzburg 0.231386 0.096208 2.405 0.016
L1.Steiermark 0.177731 0.136742 1.300 0.194
L1.Tirol 0.057084 0.090774 0.629 0.529
L1.Vorarlberg 0.018502 0.087685 0.211 0.833
L1.Wien 0.261057 0.180752 1.444 0.149
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.622066 0.111025 5.603 0.000
L1.Burgenland -0.019041 0.052742 -0.361 0.718
L1.Kärnten -0.004125 0.044663 -0.092 0.926
L1.Niederösterreich -0.061752 0.126313 -0.489 0.625
L1.Oberösterreich 0.288056 0.105169 2.739 0.006
L1.Salzburg 0.005529 0.053348 0.104 0.917
L1.Steiermark 0.009813 0.075825 0.129 0.897
L1.Tirol 0.077845 0.050335 1.547 0.122
L1.Vorarlberg 0.188699 0.048622 3.881 0.000
L1.Wien -0.097972 0.100228 -0.977 0.328
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.096179 -0.056500 0.180136 0.233414 0.008380 0.068355 -0.121518 0.116400
Kärnten 0.096179 1.000000 -0.058221 0.182899 0.093499 -0.170738 0.195835 0.023561 0.267157
Niederösterreich -0.056500 -0.058221 1.000000 0.240877 0.052288 0.161386 0.079698 0.053959 0.352508
Oberösterreich 0.180136 0.182899 0.240877 1.000000 0.254547 0.265996 0.073301 0.066574 0.048447
Salzburg 0.233414 0.093499 0.052288 0.254547 1.000000 0.126428 0.046872 0.097236 -0.061921
Steiermark 0.008380 -0.170738 0.161386 0.265996 0.126428 1.000000 0.083892 0.084760 -0.190472
Tirol 0.068355 0.195835 0.079698 0.073301 0.046872 0.083892 1.000000 0.131689 0.098000
Vorarlberg -0.121518 0.023561 0.053959 0.066574 0.097236 0.084760 0.131689 1.000000 0.076513
Wien 0.116400 0.267157 0.352508 0.048447 -0.061921 -0.190472 0.098000 0.076513 1.000000